Cross-border commodity adaptive compliance data standardization system and method

Abstract

The invention provides a cross-border commodity adaptive compliance data standardization system and method. Comprising a data preprocessing module used for mapping and converting field names and data formats of different sources; the semantic recognition module is used for recognizing and obtaining initial standardized commodity information; the compliance processing module is used for generating a supervision rule data structure associated with commodity categories, customs clearing fields and customs codes; the standard commodity archive generation module is used for filling customs clearance fields which can be deduced according to the cross-border commodity knowledge graph and the initial standardized commodity information, marking customs clearance fields which cannot be automatically determined, and generating standard commodity archive data; and the electronic tag docking module is used for establishing a corresponding relationship between the electronic tag code and the standard commodity archive data. According to the invention, unified standardized modeling of heterogeneous commodity data can be realized, the generation accuracy of compliance fields and customs clearance information is improved, and the electronic tag application and commodity file maintenance cost is reduced.

Description

Technical Field

[0001] This application relates to the field of cross-border e-commerce platform technology, and in particular to an adaptive compliance data standardization system and method for cross-border goods. Background Technology

[0002] In the cross-border e-commerce sector, especially in markets with stringent regulatory requirements such as Russia, large volumes of B2B goods typically undergo rigorous customs compliance checks. Beyond the basic information displayed on the platform's front end, multi-dimensional customs clearance information is required, including customs code, country of origin, inspection documents, product name, chemical composition, density, intended use, and material. Furthermore, the definitions and reporting standards for these fields are dynamically adjusted according to local regulatory policies. Meanwhile, domestic merchants generally rely on their own business systems or enterprise resource planning (ERP) systems to manage product data, resulting in significant differences in data structure that make it difficult to directly meet the data format and field requirements of overseas regulatory systems.

[0003] In existing technologies, cross-border e-commerce platforms often guide merchants to manually fill in or import product information by setting fixed product listing templates or providing simple field mapping rules. Some solutions introduce general machine translation services to translate product titles and descriptions into Russian for front-end display and to assist in the generation of some customs clearance documents. However, these solutions are usually based on manually maintained rule tables and static templates, and the relationships between product categories, attributes, and customs clearance fields rely on experience-based configuration, lacking the ability to comprehensively model the relationship between merchants' original data, historical customs clearance data, and overseas regulatory rules.

[0004] The existing technologies mentioned above have the following problems: First, they cannot achieve unified and refined semantic understanding and structured standardization of heterogeneous product data from different merchant systems, and inconsistencies in field meanings and units of measurement are common. Second, they lack systematic modeling capabilities for overseas regulatory rules, and after regulatory texts are updated, templates and rules often need to be manually adjusted item by item, making it difficult to respond to rule changes in a timely manner. Third, the existing solutions have low correlation between customs clearance fields and the data required for electronic tag applications, and electronic tag coding often relies on manual compilation and integration, making it difficult to form a traceable and verifiable standard product file, resulting in higher data preparation costs and error risks for merchants in cross-border scenarios. Summary of the Invention

[0005] In view of this, embodiments of this application provide a cross-border commodity adaptive compliance data standardization system and method to solve the problems of existing technologies, such as the difficulty in unifying and standardizing modeling multi-source commodity data, the inability of regulatory rules to be adaptively structured and applied, and the difficulty in automatically generating compliant commodity files for electronic tag applications.

[0006] The first aspect of this application provides an adaptive compliance data standardization system for cross-border commodities, comprising: a data preprocessing module, used to obtain raw commodity data from merchant business systems, partner enterprise resource planning systems, and platform internal systems, and to map and convert field names and data formats from different sources to form commodity input data conforming to a preset intermediate data structure; a semantic recognition module, used to generate a semantic representation of commodities based on commodity text and image information in the commodity input data, and to identify commodity categories, brands, general attributes, category-specific attributes, and customs code candidates in conjunction with a predetermined cross-border commodity knowledge graph to obtain initial standardized commodity information; and a compliance processing module, used to obtain regulatory rule text related to cross-border commodities from regulatory rule sources, and to generate a regulatory rule data structure associated with commodity categories, customs clearance fields, and customs codes. The regulatory rule data structure is transformed into compliance rule information for field item determination and field constraint checks. The standard commodity file generation module is used to determine the target field set of the target commodity under the target regulatory rule based on the initial standardized commodity information and compliance rule information. It fills in the customs clearance fields that can be deduced from the cross-border commodity knowledge graph and the initial standardized commodity information, and marks the customs clearance fields that cannot be automatically determined. It generates standard commodity file data containing category information, brand information, attribute information, warehousing and logistics information, adaptation relationship information, and customs clearance information. The electronic tag docking module is used to extract the fields required for electronic tag application based on the standard commodity file data, generate electronic tag application data, interact with the external electronic tag system through a preset interface to obtain the electronic tag code, and establish the correspondence between the electronic tag code and the standard commodity file data.

[0007] The second aspect of this application provides a method for adaptive compliance data standardization of cross-border commodities based on the system of the first aspect, including: acquiring raw commodity data from merchant business systems, partner enterprise resource planning systems, and platform internal systems, and mapping and converting field names and data formats from different sources to form commodity input data conforming to a preset intermediate data structure; generating a semantic representation of commodities based on commodity text and image information in the commodity input data, and identifying commodity categories, brands, general attributes, category-specific attributes, and customs code candidates in conjunction with a predetermined cross-border commodity knowledge graph to obtain initial standardized commodity information; and acquiring regulatory rule text related to cross-border commodities from regulatory rule sources to generate a number of regulatory rules associated with commodity categories, customs clearance fields, and customs codes. According to the structure, the regulatory rule data structure is transformed into compliance rule information for field item determination and field constraint checking; based on the initial standardized commodity information and compliance rule information, the target field set of the target commodity under the target regulatory rule is determined, the customs clearance fields that can be deduced from the cross-border commodity knowledge graph and the initial standardized commodity information are filled, and the customs clearance fields that cannot be automatically determined are marked, generating standard commodity file data containing category information, brand information, attribute information, warehousing and logistics information, adaptation relationship information, and customs clearance information; based on the standard commodity file data, the fields required for electronic tag application are extracted, electronic tag application data is generated, and electronic tag codes are obtained by interacting with external electronic tag systems through preset interfaces, and the correspondence between electronic tag codes and standard commodity file data is established.

[0008] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects: The data preprocessing module retrieves raw product data from merchant business systems, partner enterprise resource planning systems, and the platform's internal systems. It maps and transforms field names and data formats from different sources to form product input data conforming to a pre-defined intermediate data structure. The semantic recognition module generates semantic representations of products based on textual and image information from the input data. It then uses a pre-defined cross-border product knowledge graph to identify product categories, brands, general attributes, category-specific attributes, and customs code candidates, obtaining initial standardized product information. The compliance processing module retrieves regulatory rule texts related to cross-border products from regulatory rule sources, generates a regulatory rule data structure associated with product categories, customs clearance fields, and customs codes, and transforms this data structure into data for fields. The application includes: a compliance rule information module for item determination and field constraint checking; a standard commodity file generation module, used to determine the target field set of the target commodity under the target regulatory rules based on initial standardized commodity information and compliance rule information; filling in customs clearance fields that can be derived from the cross-border commodity knowledge graph and initial standardized commodity information; marking customs clearance fields that cannot be automatically determined; and generating standard commodity file data containing category information, brand information, attribute information, warehousing and logistics information, adaptation relationship information, and customs clearance information; and an electronic tag integration module, used to extract the fields required for electronic tag application based on the standard commodity file data, generate electronic tag application data, interact with external electronic tag systems through a preset interface to obtain electronic tag codes, and establish a correspondence between electronic tag codes and standard commodity file data. This application enables unified standardized modeling of heterogeneous commodity data, improves the accuracy of compliance field and customs clearance information generation, and reduces the cost of electronic tag application and commodity file maintenance. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a schematic diagram of the structural composition of the cross-border commodity adaptive compliance data standardization system provided in the embodiments of this application; Figure 2 This is a flowchart illustrating the adaptive compliance data standardization method for cross-border goods provided in this application embodiment. Detailed Implementation

[0011] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0012] In existing technologies, cross-border e-commerce platforms targeting markets with high regulatory requirements, such as Russia, typically assist merchants in filling out product information through fixed product listing templates, simple field mapping tables, and general machine translation services. Merchants maintain product data using their own business systems or enterprise resource planning systems, and there are significant differences in field naming, data structures, and units of measurement between different systems. Platforms often map merchant fields to platform fields through rule tables or configuration items, and merchants manually supplement customs clearance information such as customs codes, customs clearance names, chemical compositions, uses, and materials. Meanwhile, Russian customs and electronic tag-related regulatory rules are published and updated in the form of announcements or documents. Platforms usually rely on manual interpretation to adjust templates and verification rules, and electronic tag applications also largely depend on manual field organization, message assembly, and integration with external systems.

[0013] Against this backdrop, existing technologies suffer from the following main technical problems: First, they lack the ability to achieve a unified semantic understanding and standardized modeling of heterogeneous product data from different merchant systems, resulting in inconsistent field meanings and units of measurement, making it difficult to form structured standard product profiles. Second, they lack a systematic modeling and adaptive application mechanism for overseas regulatory rules, meaning that updated regulatory texts cannot be automatically converted into executable field item determination and field constraint logic, making it difficult to support the timely determination and verification of compliant fields. Third, there is a lack of close technical binding between standard product profiles and electronic tag applications, making it impossible to automatically extract electronic tag application fields, generate application data, and establish a stable coding mapping relationship based on standardized product data, resulting in high costs and high error rates in product profile maintenance and electronic tag application processes.

[0014] In view of the problems existing in the prior art, this application proposes a cross-border commodity adaptive compliance data standardization system and method, which introduces functional modules such as multi-source commodity data preprocessing, multimodal semantic recognition, compliance processing, standard commodity file generation, and electronic tag docking in the system architecture. The data preprocessing module performs semantic mapping and transformation on field names and data formats from different sources based on field semantic models and cross-border commodity knowledge graphs to form unified commodity input data. The semantic recognition module encodes commodity text and image information into semantic representations and retrieves category, brand, attribute, and customs code candidates from the cross-border commodity knowledge graph to generate initial standardized commodity information. The compliance processing module performs semantic parsing and rule triple modeling on regulatory rule texts to form compliance rule information associated with commodity categories, customs clearance fields, and customs codes. The standard commodity file generation module determines the target field set under the constraints of compliance rule information, derives automatically identifiable customs clearance fields based on the knowledge graph and initial standardized commodity information, marks fields that cannot be automatically determined, and generates structurally complete standard commodity file data. The electronic tag integration module automatically extracts application fields based on the standard commodity file data and preset electronic tag field templates, generates electronic tag application data, interacts with external electronic tag systems to obtain electronic tag codes, and establishes a correspondence between electronic tag codes and standard commodity file data.

[0015] By adopting the above technical solutions, this application can achieve unified and standardized modeling of heterogeneous commodity data at the system level, improve the accuracy and consistency of determining commodity categories, attributes, customs clearance fields, and electronic tag application fields, and realize the adaptive application of regulatory rules in field item determination and field constraint checks through structured modeling and rule version management of regulatory rules. On this basis, standard commodity file data bound to electronic tag codes is automatically generated, reducing the manual costs and error risks of commodity file maintenance and electronic tag application, and providing a stable data foundation for the compliant entry of cross-border commodities under the target regulatory environment.

[0016] The following detailed description, in conjunction with the accompanying drawings and specific embodiments, outlines the specific composition and functions of the cross-border commodity adaptive compliance data standardization system provided in this application.

[0017] Figure 1 This is a schematic diagram of the structural composition of the cross-border commodity adaptive compliance data standardization system provided in this application embodiment, as shown below. Figure 1 As shown, the cross-border commodity adaptive compliance data standardization system may specifically include the following modules: The data preprocessing module 101 is used to obtain raw product data from the merchant's business system, the partner's enterprise resource planning system and the platform's internal system, and to map and convert the field names and data formats from different sources to form product input data that conforms to the preset intermediate data structure. The semantic recognition module 102 is used to generate a semantic representation of the product based on the product text information and image information in the product input data, and to identify the product category, brand, general attributes, category-specific attributes and customs code candidates by combining the predetermined cross-border product knowledge graph, so as to obtain the initial standardized product information. The compliance processing module 103 is used to obtain regulatory rule texts related to cross-border commodities from the regulatory rule source, generate regulatory rule data structures associated with commodity categories, customs clearance fields and customs codes, and transform the regulatory rule data structures into compliance rule information for field item determination and field constraint checks. The standard commodity file generation module 104 is used to determine the target field set of the target commodity under the target regulatory rules based on the initial standardized commodity information and compliance rule information. It fills in the customs clearance fields that can be deduced from the cross-border commodity knowledge graph and the initial standardized commodity information, marks the customs clearance fields that cannot be automatically determined, and generates standard commodity file data containing category information, brand information, attribute information, warehousing and logistics information, adaptation relationship information and customs clearance information. The electronic tag integration module 105 is used to extract the fields required for electronic tag application based on standard commodity file data, generate electronic tag application data, interact with external electronic tag systems through preset interfaces to obtain electronic tag codes, and establish the correspondence between electronic tag codes and standard commodity file data.

[0018] In some embodiments, field names and data formats from different sources are mapped and converted to form product input data that conforms to a preset intermediate data structure, including: Based on the preset field semantic model, semantic features are extracted from the field names and field contents of each original commodity data to obtain the semantic representation of fields related to commodity category, brand, attributes, warehousing and logistics information and customs clearance information; The semantic representation of the fields is matched with the intermediate field nodes in the pre-defined cross-border commodity knowledge graph used to describe the preset intermediate data structure. Based on the matching results, the field mapping relationship between each original field and the target field in the preset intermediate data structure is determined. Based on the field mapping relationship, the original product data is renamed at the field level, the data type is converted, and the unit of measurement is normalized. Then, the processed fields are restructured according to the preset intermediate data structure to generate product input data.

[0019] Specifically, the system pre-builds a field semantic model. The field semantic model uses historical product data, manually annotated field samples, and existing fields in the cross-border product knowledge graph as training corpus. By semantically modeling the field name, field value examples, and the context description of the field, each field name and field content is mapped to a semantic feature vector.

[0020] During semantic feature extraction, the data preprocessing module iterates through each field in the original product data, inputting both the field name string and the field content text into the field semantic model to obtain semantic representations of fields related to product category, brand, attributes, warehousing and logistics information, and customs clearance information. For example, for fields such as "item number," "product name," "material," "gross weight," and "HS code" in a merchant system, the field semantic model outputs corresponding semantic feature vectors to indicate that the field is more related to product identification, product name, material attributes, logistics weight information, or customs code information.

[0021] In some examples, the cross-border commodity knowledge graph pre-configures a set of intermediate field nodes for a preset intermediate data structure. These intermediate field nodes are used to uniformly describe standard fields such as commodity category fields, brand fields, general attribute fields, warehousing and logistics fields, and customs clearance information fields. Each intermediate field node is associated with a field name alias, typical value instances, and semantic relationships with other field nodes in the graph.

[0022] During field matching, the data preprocessing module calculates the similarity between the aforementioned semantic representations of the fields and the semantic vectors of intermediate field nodes in the cross-border commodity knowledge graph. It then combines this with the relationships between intermediate field nodes to determine the target intermediate field node corresponding to each original field. For candidate intermediate field nodes with similarity exceeding a preset threshold, the module selects the intermediate field node with the highest similarity that does not conflict with already matched fields as the mapping target. This determines the one-to-one or many-to-one field mapping relationship between each original field and the target field in the preset intermediate data structure.

[0023] Furthermore, after the field mapping relationship is determined, the data preprocessing module performs field-level renaming, data type conversion, and unit normalization on each original product data based on the field mapping relationship. For fields with inconsistent names but the same semantics, such as "product name" in one merchant system and "prod_name" in another merchant system, the system uniformly renames them to the "product title" field in the preset intermediate data structure; for cases where the value type of the same field is different, such as some systems using string form to represent weight and some systems using numeric form to represent weight, the system uniformly converts the field value to numeric form and attaches the standard unit of measurement; for cases where the unit of measurement is inconsistent, such as some systems recording net weight in "g" and some systems recording gross weight in "kg", the system performs unit normalization according to the unit rules and conversion factors recorded in the field mapping relationship, uniformly converting all weight-related fields to the unit specified in the preset intermediate data structure.

[0024] After renaming, type conversion, and unit normalization, the data preprocessing module restructures the processed fields according to the field hierarchy and field grouping rules in the preset intermediate data structure. It encapsulates a group of fields belonging to the same product identifier into a single product input data record, aligns product input data from different sources to a unified set of fields and field format, and finally generates product input data for subsequent modules to use.

[0025] In some embodiments, the system further includes: The cross-border commodity knowledge graph construction module is used to build a cross-border commodity knowledge graph based on commodity input data, historical customs clearance data, customs code data, and customs clearance field definitions. This graph includes commodity entities, category entities, brand entities, customs code entities, customs clearance field entities, adaptation relationship entities, and the relationships between them. The module also updates and maintains the cross-border commodity knowledge graph.

[0026] Specifically, the cross-border commodity knowledge graph construction module first extracts basic entity information from the commodity input data. For each piece of commodity input data, the module identifies the commodity identifier field, category field, brand field, and key attribute fields. It generates commodity entity nodes from the records corresponding to the commodity identifier field, maps the standard category values ​​corresponding to the category field to category entity nodes, maps the standard brand values ​​corresponding to the brand field to brand entity nodes, and records attribute fields related to core attributes as attribute sets for commodity entities. Simultaneously, the module establishes "belongs to" relationships between commodity entities and category entities, and "belongs to brand" relationships between commodity entities and brand entities, thus forming a basic commodity classification and brand affiliation structure in the graph.

[0027] Building upon this foundation, the cross-border commodity knowledge graph construction module injects customs code entities into the graph using customs code data. The module extracts customs codes at all levels, corresponding commodity scope descriptions, and regulatory requirement summaries from existing customs code data, generating a customs code entity node for each customs code. It then matches typical categories and key attributes mentioned in the customs code description with existing category entities and attribute information, establishing a "corresponding customs code" relationship between customs code entities and matching category entities. For customs code fields in the commodity input data that already contain manual annotations or system inferences, the module further establishes a "customs code adopted" relationship between commodity entities and their corresponding customs code entities, recording the source identifier of this relationship to distinguish between historical manual declaration results and system inference results.

[0028] Furthermore, to integrate customs clearance fields into a unified graph structure, the cross-border commodity knowledge graph construction module also constructs customs clearance field entity nodes based on the customs clearance field definitions. The module extracts various customs clearance field names, value types, typical value examples, and applicable scopes with customs codes and categories from a pre-defined customs clearance field definition library. It generates a customs clearance field entity node for each definition and establishes a "field applicability" relationship between the customs clearance field entity and its corresponding customs code entity and category entity. For some customs clearance fields whose values ​​depend on attributes such as commodity material, use, density, and packaging form, the module further establishes a "value dependency" relationship between the customs clearance field entity and related attributes in the graph based on the dependency conditions in the customs clearance field definition, for use in subsequent customs clearance field derivation model calls.

[0029] In some examples, to support adaptation modeling between parts and complete machines, and vehicles and components, the cross-border commodity knowledge graph construction module constructs adaptation relationship entities based on commodity input data and historical adaptation relationship records. The module extracts fields such as part codes, complete machine codes, vehicle identification numbers, original equipment manufacturer (OEM) part codes, and replacement part codes from commodity input data or historical business systems. Each record representing the relationship between a component and a complete machine is abstracted into an adaptation relationship entity node, and relationship edges such as "adapted to" and "replaced by" are established between the adaptation relationship entity and the corresponding commodity entity. In this way, the cross-border commodity knowledge graph can not only express category and customs clearance information at the single-item level, but also reflect the configuration and replacement relationships between different commodities on a layer, providing a reference for subsequently deriving customs clearance fields or customs codes in specific adaptation scenarios.

[0030] Regarding the updating and maintenance of the knowledge graph, in some examples, the cross-border commodity knowledge graph construction module is also configured with an incremental update mechanism and a version management mechanism. When the system receives new commodity input data, the module first determines whether core fields such as commodity identifier, category, and brand correspond to existing entity nodes. For existing commodity entities, the module updates the attribute set of the commodity entity according to the new attribute field values ​​and adjusts the association relationship between the commodity entity and category and brand entities as needed. For non-existent commodity entities, the module creates new commodity entity nodes and establishes initial association relationships. When regulatory rules are updated, causing changes in customs code definitions or customs clearance field definitions, the module assigns new version identifiers to the adjusted customs code entities, customs clearance field entities, and their association relationships through the version management mechanism, while retaining historical version nodes and relationships so that the module can select the appropriate graph version for reasoning when processing historical commodity data or replaying historical customs clearance scenarios.

[0031] Through the above embodiments, the cross-border commodity knowledge graph construction module can integrate commodity input data, customs code data, customs clearance field definitions, and adaptation relationship information in a unified graph structure. This enables standardized modeling and association expression of commodity entities, category entities, brand entities, customs code entities, customs clearance field entities, and adaptation relationship entities. It provides a consistent knowledge foundation for the semantic recognition module, compliance processing module, and standard commodity file generation module, thereby improving the accuracy and scalability of semantic association judgment in subsequent category recognition, customs code recommendation, and customs clearance field derivation processes.

[0032] In some embodiments, by combining a predetermined cross-border commodity knowledge graph, commodity categories, brands, and general attributes, category-specific attributes, and customs code candidates are identified to obtain initial standardized commodity information, including: Based on the product text information and image information, a product semantic representation is generated, and the product semantic representation is mapped to the semantic space corresponding to the cross-border product knowledge graph to obtain a product embedding vector that is compatible with the graph nodes; In the cross-border commodity knowledge graph, the commodity embedding vector is used as the query vector. A set of candidate nodes is retrieved from category nodes, brand nodes, attribute nodes and customs code nodes. Based on the similarity relationship between the candidate nodes and the commodity embedding vector and the node association path preset in the graph, the set of candidate nodes is filtered and combined to determine the target category node, target brand node, target attribute node and target customs code candidate node. Based on the node identifier information of the target category node, target brand node, target attribute node, and target customs code candidate node, generate initial standardized product information containing category field, brand field, general attribute field, category-specific attribute field, and customs code candidate field.

[0033] Specifically, the semantic recognition module first encodes the product text information, converting Chinese characters, numbers, and common abbreviations in the title, selling points, and parameter descriptions into unified text vector representations. During this process, the module can prioritize key phrases in the text based on existing category names, brand names, and typical attribute vocabularies in the cross-border product knowledge graph, thereby enhancing semantic features related to product categories, brands, and attributes.

[0034] Similarly, for product image information, the module uses a pre-defined image feature extraction network to convert product images into image feature vectors, extracting high-dimensional features related to the product's appearance, color, and structural layout. Subsequently, the semantic recognition module concatenates or aligns the text vector and image feature vector according to a pre-defined fusion strategy to obtain a single product semantic representation vector. This semantic representation is then mapped to the semantic space corresponding to the cross-border product knowledge graph through a semantic space mapping layer, generating a product embedding vector compatible with the graph nodes.

[0035] Furthermore, after generating the product embedding vector, the semantic recognition module uses this product embedding vector as the query vector to perform similarity retrieval for different types of nodes in the cross-border commodity knowledge graph. For the category node set, the module calculates the similarity between the product embedding vector and the vectors of each candidate category node, and selects several category nodes with high similarity as the category candidate set; for the brand node set, the module calculates the similarity based on the vector representation of the brand node in the graph and the brand name alias information, and obtains the brand candidate set; for attribute nodes and customs code nodes, the module combines the key attribute words, technical parameters, and the co-occurrence relationship between certain attributes and customs codes in the historical customs clearance data reflected in the product semantic representation to form the attribute candidate set and the customs code candidate set.

[0036] After candidate retrieval is completed, the semantic recognition module does not directly select based on a single similarity score. Instead, it combines the preset node association paths in the cross-border commodity knowledge graph to filter and combine candidate nodes. For example, when the product embedding vector of a certain home appliance product has a high similarity to a certain category node "portable electric kettle", the module will also check whether attribute nodes such as "rated power", "rated voltage", and "capacity" that are strongly associated with that category node are supported in the product semantic representation, thereby correcting and confirming the target category node.

[0037] In some examples, to further improve the reliability of brand recognition and customs code candidate recognition, the semantic recognition module combines the node relationships in the cross-border commodity knowledge graph for joint decision-making. When there are multiple similar brand nodes in the brand candidate set corresponding to the product embedding vector, the module will prioritize selecting the brand node that has a stable "common pairing" relationship with the target category node in the graph or has more historical customs clearance records as the target brand node.

[0038] Similarly, for customs code candidate nodes, the semantic recognition module considers not only the direct similarity between the product embedding vector and the customs code node vector, but also the completeness and consistency of the association path between the customs code node and the target category node and the target attribute node. For example, for a "synthetic fabric down jacket", if the product embedding vector has a similarity to two different customs code nodes, the module will prioritize selecting the customs code node that has established a complete attribute link with attribute nodes such as "clothing", "outerwear", "synthetic fiber", and "filling" as the target customs code candidate node.

[0039] Furthermore, after identifying the target category node, target brand node, target attribute node, and target customs code candidate node, the semantic recognition module generates initial standardized product information based on the node identification information and attribute definition information carried by these nodes.

[0040] For example, in some specific cases, for a product input data item, the original title is "1200W Stainless Steel Electric Kettle 1.7L," and the image shows the appearance of a countertop electric kettle. The module, through the aforementioned process, identifies the target category node as "Small Kitchen Appliances / Electric Kettle," the target brand node as a specific brand entity, and the target attribute nodes as "Rated Power = 1200W," "Capacity = 1.7L," and "Material = Stainless Steel." The target customs code candidate node is a code associated with the category "Electric Heating Household Appliances." The semantic recognition module maps this node information into structured fields, generating an initial standardized product information record containing category fields, brand fields, general attribute fields, category-specific attribute fields, and customs code candidate fields, for subsequent use by the compliance processing module and the standard product file generation module.

[0041] Through the above embodiments, the semantic recognition module utilizes the multimodal fusion representation of commodity text information and image information, and combines the association paths between category nodes, brand nodes, attribute nodes and customs code nodes in the cross-border commodity knowledge graph to complete the collaborative recognition of commodity category, brand, attribute and customs code candidates in a unified semantic space. This can improve the consistency and accuracy of commodity classification and attribute recognition, and provide a semantically coordinated and unified initial standardized commodity information foundation for subsequent customs clearance field derivation and compliance rule application.

[0042] In some embodiments, regulatory rule texts related to cross-border goods are obtained from a regulatory rule source, a regulatory rule data structure associated with commodity categories, customs clearance fields, and customs codes is generated, and the regulatory rule data structure is transformed into compliance rule information for field item determination and field constraint checks, including: Based on a pre-defined rule semantic model, the regulatory rule text is segmented, parsed, and semantically annotated. Semantic fragments involving commodity categories, customs clearance fields, and customs codes are extracted to generate corresponding rule semantic representations. The semantic representation of the rules is associated and matched with the commodity category nodes, customs clearance field nodes, and customs code nodes in the cross-border commodity knowledge graph. A set of rule triples is constructed with commodity category nodes, customs clearance field nodes, and customs code nodes as the main body and field mandatory relationship, field combination relationship, and value constraint relationship as the object. A regulatory rule data structure containing rule nodes and their association with commodity category, customs clearance field, and customs code is generated. Based on the preset rule description template, the rule nodes and relationships in the regulatory rule data structure are converted into rule description units containing field item determination logic and field constraint checking logic. Each rule description unit is assigned a rule version identifier and applicable category scope, forming compliance rule information used to drive the standard commodity file generation module to determine the target field set and perform field constraint verification.

[0043] Specifically, the compliance processing module is pre-trained with a rule semantic model. The rule semantic model uses historical regulatory rule texts, manually annotated clause samples, and categories and customs clearance fields in the cross-border commodity knowledge graph as corpus. It performs semantic modeling on clause titles, clause content, field examples, and appendix descriptions to identify commodity categories, customs clearance fields, and customs code requirements involved in different clauses.

[0044] During the parsing process, the compliance processing module first segments the regulatory rule text according to chapter titles, clause numbers, and punctuation structure, dividing the entire regulatory document into multiple rule fragments. Subsequently, each rule fragment is input into the rule semantic model for semantic annotation, identifying commodity category terms, field names, mandatory field conditions, value range descriptions, and referenced customs code information that appear in the fragment, thereby generating a rule semantic representation corresponding to that rule fragment.

[0045] For example, in a regulatory clause concerning "clothing and clothing accessories", the rule semantic model can identify that its applicable category is "clothing / outerwear", and the relevant customs clearance fields include "fabric composition", "weight", "purpose" and "gender", and identify several customs code segments corresponding to the clause.

[0046] Furthermore, after obtaining the semantic representation of the rule, the compliance processing module associates and matches the semantic representation with the commodity category nodes, customs clearance field nodes, and customs code nodes in the cross-border commodity knowledge graph. On the one hand, the module matches the category terms extracted from the text with the category entity names, aliases, and parent category relationships in the knowledge graph to determine the set of commodity category nodes to which the clause applies. On the other hand, the module matches the field names identified in the semantic annotation results with the customs clearance field entities in the graph to determine the customs clearance field nodes involved in the clause, and identifies the mandatory and combined relationships of fields based on the semantic context. For example, "fabric composition" and "weight" must be filled in simultaneously or at least one of them must be filled in. At the same time, the module associates the customs code range or example codes appearing in the clause with the customs code entities.

[0047] Based on the matching results above, the compliance processing module uses commodity category nodes, customs clearance field nodes, and customs code nodes as the main nodes, and field mandatory relationships, field combination relationships, and value constraint relationships as the objects to construct a set of rule triples, such as (Category = Outerwear, Field = Fabric Composition, Relationship = Mandatory), (Field = Weight, Field = Density, Relationship = Value Dependency), (Customs Code = 6101, Field = Material, Relationship = Value Constraint), etc., and generates a regulatory rule data structure containing rule nodes and their association with commodity category, customs clearance field, and customs code.

[0048] In some examples, to facilitate invocation by the standard commodity file generation module, the compliance processing module also converts the regulatory rule data structure into rule description units. The system pre-sets rule description templates to describe the logic for determining field items and the logic for checking field constraints. The compliance processing module traverses the rule nodes and their associated triples in the regulatory rule data structure, organizing information such as "applicable category range," "associated customs code range," "set of required fields," "set of conditional fields," "set of optional fields," "field combination relationships," and "value constraints" into structured rule description units according to the template requirements.

[0049] For example, for the aforementioned "clothing" clause, a rule description unit can be formed, which internally records the applicable category as the set of "clothing / outerwear" category nodes in the map, the applicable customs code segment as several "61××" code segments, the set of required fields including "fabric composition" and "purpose", the set of conditional fields including "weight" and "either weight or density", and the numerical range or enumeration value constraints corresponding to the above fields.

[0050] Furthermore, after the rule description unit is generated, the compliance processing module assigns a rule version identifier and an applicable category scope identifier to each rule description unit, and records which version of the regulatory rule text the rule description unit originates from. The rule version identifier can be a combination of an incrementing version number and a timestamp to distinguish rule content that took effect at different times. The applicable category scope identifier corresponds to the category node identifier in the cross-border commodity knowledge graph, clarifying that the rule description unit is only effective for commodities in a specific category or category subtree.

[0051] Ultimately, the compliance rule information composed of all rule description units is stored in the rule base, providing the standard commodity profile generation module with field item determination logic and field constraint check logic when processing specific commodities. This enables the corresponding rule description unit to be quickly retrieved based on the commodity category and candidate customs code when determining the target field set and performing field compliance verification.

[0052] Through the above embodiments, the compliance processing module can automatically parse the regulatory rule text expressed in natural language into a set of rule triples associated with nodes of the cross-border commodity knowledge graph. Based on this, it generates rule description units and compliance rule information containing field item determination logic and field constraint checking logic. This realizes the transformation of regulatory rules from unstructured text to structured executable rules, thereby providing the standard commodity file generation module with a basis for determining field sets and verifying field compliance that can be adaptively called according to category and customs code, improving the efficiency and consistency of rule application after regulatory rule updates.

[0053] In some embodiments, based on initial standardized product information and compliance rule information, the set of target fields for the target product under the target regulatory rule is determined, including: Based on the category field and customs code candidate field in the initial standardized commodity information, rule description units that match the target commodity category range and customs code range are selected from the compliance rule information to form a target rule set; Based on the field item determination logic in the target rule set, a field constraint graph structure is constructed with customs clearance field, basic attribute field, and warehousing and logistics field as nodes, and field mandatory relationship, condition trigger relationship, and field combination relationship as edges; Based on the attribute field values ​​identified in the initial standardized product information, the state of the condition triggering relationship associated with the attribute values ​​is determined. Through traversal and dependency reasoning of the field constraint graph structure, the sets of required fields, condition fields, and optional fields under the target regulatory rules are determined respectively, and the sets of required fields and condition fields are merged to determine the target field set.

[0054] Specifically, the standard commodity file generation module determines the logic based on the field items in the target rule set and constructs a field constraint graph structure. The field constraint graph structure uses customs clearance fields, basic attribute fields, and warehousing and logistics fields as graph nodes, such as field nodes for "rated power", "rated voltage", "capacity", "material", "use", "packaging specifications", and "gross weight". At the same time, it maps the mandatory field relationships, conditional trigger relationships, and field combination relationships described in the target rule set to directed or undirected edges in the graph.

[0055] For example, if the target rule set specifies that "rated power" and "rated voltage" are mandatory fields, then a mandatory relationship edge from the category condition to "rated power" and "rated voltage" is set in the field constraint diagram; if the rule description unit specifies that "nickel content needs to be filled in when the material is stainless steel", then a condition trigger relationship edge from the "material" node to the "nickel content" node is established in the field constraint diagram, and the trigger condition is recorded; if the rule requires that "packaging specifications and quantity per box need to appear in pairs", then a field combination relationship edge is established between the "packaging specifications" node and the "quantity per box" node to represent the mutual dependence between fields.

[0056] Furthermore, after constructing the field constraint graph structure, the standard product profile generation module combines the attribute field values ​​identified in the initial standardized product information to determine the state of the condition triggering relationships in the graph, and performs traversal and dependency reasoning on the field constraint graph structure.

[0057] For example, in some examples, the initial standardized product information includes attribute field values ​​such as "material = stainless steel", "capacity = 1.7L", and "use = household water boiling". The module first activates the required field relationship matching the category and code candidate fields based on the category field and customs code candidate fields, and includes the "rated power", "rated voltage", "capacity", and "material" nodes into the set of required fields. Then, the module triggers the conditional trigger relationship from "material" to "nickel content" based on the value "material = stainless steel", and promotes the "nickel content" node from a candidate field to a condition field. Then, according to the packaging-related rule description, the "packaging specifications" and "quantity per box" nodes are included in the set of condition fields.

[0058] During the reasoning process, the system recursively analyzes the field dependencies at all levels derived from category conditions, attribute values, and combination relationships by performing a depth-first or breadth-first traversal of the field constraint graph. It then summarizes the sets of required fields, conditional fields, and optional fields under the target regulatory rules, and finally merges the sets of required fields and conditional fields to determine the target field set that needs to be monitored and processed under the target regulatory rules for the target product. This set is used for subsequent customs clearance field filling and pending confirmation field marking.

[0059] Through the above embodiments, the standard commodity file generation module, driven by compliance rule information, uniformly expresses mandatory relationships, conditional trigger relationships, and field combination relationships in the form of a field constraint graph structure. It can automatically determine the target field set by graph traversal and dependency reasoning, taking into account commodity categories, customs code candidates, and attribute values. This allows the field requirements under different categories and different regulatory rules to be dynamically and precisely calculated, thereby improving the completeness and accuracy of the field set determination and reducing the risk of omissions and inconsistencies caused by relying on manual experience to configure field rules.

[0060] In some embodiments, customs clearance fields that can be deduced based on cross-border commodity knowledge graphs and initial standardized commodity information are populated, and customs clearance fields that cannot be automatically determined are marked, including: Based on the category field, attribute field, and customs code candidate field in the initial standardized commodity information, the clearance field nodes that have a path associated with the target commodity category node, target attribute node, and target customs code candidate node are retrieved in the cross-border commodity knowledge graph to construct a clearance field candidate set for the target commodity. For each customs clearance field node in the candidate set of customs clearance fields, the corresponding derivation confidence is calculated using a preset derivation model based on the field value patterns, field dependencies, and attribute values ​​recorded in the cross-border commodity knowledge graph and the initial standardized commodity information. Write the customs clearance fields with a derivation confidence level higher than the first preset threshold into the standard commodity file data, and calculate the customs clearance field filling value according to the value pattern in the cross-border commodity knowledge graph or the relevant fields in the initial standardized commodity information. At the same time, record the field source identifier and rule reference identifier for each filled customs clearance field. Customs clearance fields with a derivation confidence level lower than the second preset threshold are set to a pending confirmation state, and field tagging information is generated for customs clearance fields in the pending confirmation state. The field tagging information includes recommended values, derivation confidence level, and attribute field identifiers that affect the derivation of the customs clearance field. The first preset threshold is higher than the second preset threshold.

[0061] Specifically, it is necessary to first explain the concepts of "candidate set of customs clearance fields", "field value pattern", "field dependency relationship", "derivation model" and "derivation confidence". The candidate set of customs clearance fields refers to a set of customs clearance field nodes in the cross-border commodity knowledge graph that have semantic or path associations with the category, attributes and candidate customs codes of a certain target commodity. For example, for clothing, the candidate set of customs clearance fields may include "fabric composition", "weight", "density", "use", "applicable people", "whether it has a lining" and so on.

[0062] The field value pattern refers to the value type, typical value distribution, and statistical co-occurrence relationship with other attributes for each customs clearance field record in the knowledge graph. For example, the "fabric composition" field is enumerated and common values ​​are "cotton", "polyester", "nylon", etc., while the "density" field is numerical and has different value ranges under different fabrics.

[0063] Field dependency refers to the logical dependency between customs clearance fields and commodity attribute fields. For example, the value of "density" depends on "weight" and "fabric composition", and the value of "whether it is a dangerous product" depends on "composition" and "flash point".

[0064] An inference model refers to a type of model that predicts or infers the values ​​of customs clearance fields based on initial standardized commodity information and the aforementioned patterns and dependencies in a knowledge graph. It can be a rule-based inference model, a probabilistic model based on statistical learning, or a combination of both. Inference confidence is a score used to quantify the reliability of the inference result, typically ranging from 0 to 1, with higher values ​​indicating more reliable results.

[0065] In the actual derivation process, the customs clearance field derivation and marking submodule first locates the corresponding target commodity category node, target attribute node and target customs code candidate node in the cross-border commodity knowledge graph based on the category field, attribute field and customs code candidate field in the initial standardized commodity information.

[0066] For example, for a "synthetic fiber down jacket" product, the initial standardized product information might include the category field "clothing / outerwear," the attribute fields "fabric material = polyester fiber," "filling = down," and "suitable for adults," as well as the code segment corresponding to the clothing / outerwear category in the candidate customs code field. The module finds the corresponding category entity nodes, attribute entity nodes, and customs code entity nodes in the knowledge graph based on these fields, and retrieves customs clearance field nodes in the graph that have path connections to these nodes. This constructs a candidate set of customs clearance fields for the product, such as "fabric composition," "weight," "density," "suitable season," "whether it is a set," and "thermal insulation performance level."

[0067] Furthermore, after constructing the candidate set of customs clearance fields, the customs clearance field derivation and tagging submodule calculates the corresponding derivation confidence for each customs clearance field node in the candidate set, based on the field value patterns, field dependencies, and attribute values ​​recorded in the cross-border commodity knowledge graph and the initial standardized commodity information, by calling the preset derivation model.

[0068] In one embodiment, the inference model can operate as follows: First, based on the field dependencies recorded in the knowledge graph, the attribute field values ​​related to the customs clearance field are used as input features. For example, for the "density" field, the input features may include "weight", "fabric composition", "fabric structure", etc. Second, combined with the actual values ​​of the customs clearance field for the same or similar categories of goods in historical customs clearance data, the joint distribution between each combination feature and the customs clearance field value is statistically analyzed. Then, using the joint distribution or the trained classification model, the possible values ​​of the customs clearance field for the current target product are scored to obtain the probability or similarity of each candidate value, and the highest score and its credibility are normalized to derive the inference confidence.

[0069] In some examples, for enumerated clearance fields such as "applicable season", the derivation model can output the probability distribution of each candidate season label; for numerical clearance fields such as "density", the derivation model can output an estimated interval and the corresponding confidence level.

[0070] Furthermore, after obtaining the derivation confidence level of each customs clearance field node, the customs clearance field derivation and labeling submodule classifies the customs clearance fields according to a first preset threshold and a second preset threshold. For customs clearance fields with a derivation confidence level higher than the first preset threshold, the module treats them as fields that can be automatically filled and directly writes them into the standard commodity file data.

[0071] During the writing process, the module calculates the customs clearance field fill values ​​based on the field value patterns in the cross-border commodity knowledge graph and the relevant fields in the initial standardized commodity information. For example, for the "fabric composition" field, it can be directly filled with "polyester fiber" based on "fabric material = polyester fiber". For the "density" field, it can combine attributes such as "weight" and "fabric structure" to obtain a reasonable value range using formulas or table lookups, and select the representative value of the range as the fill value.

[0072] At the same time, the module records a field source identifier for each filled customs clearance field to indicate whether the field is derived from the system or manually entered by the merchant, and records a rule reference identifier to indicate the knowledge graph node and rule description unit version used in this derivation process, so as to ensure that the derivation basis can be clearly identified during subsequent review and traceability.

[0073] For customs clearance fields with a derivation confidence level below the second preset threshold, the customs clearance field derivation and tagging submodule sets them to a pending confirmation state. Instead of directly writing a confirmed value, it generates field tagging information for the field. The field tagging information includes at least the recommended value, the derivation confidence level, and the attribute field identifiers that affect the derivation of the customs clearance field. For example, for the "applicable season" field, if the derivation model believes that the probability difference between "spring and autumn" and "winter" is small and the overall confidence level is low, the module will provide a list of recommended candidate values ​​with corresponding confidence scores. At the same time, the tagging information will indicate that the derivation of this field mainly depends on attribute fields such as "fabric thickness," "filling type," and "usage scenario keywords in the product description," making it easier for manual reviewers to quickly understand the reasons for the recommendation and to confirm or correct it.

[0074] In other examples, for customs clearance fields whose derived confidence levels are between the second preset threshold and the first preset threshold, the system can choose, based on the platform configuration, whether to mark them as "suggested values" after automatic filling or to uniformly treat them as fields to be confirmed, providing only recommended values ​​and reference explanations, with final confirmation by business personnel.

[0075] Through the above embodiments, the customs clearance field derivation and tagging submodule utilizes the field value patterns and field dependencies in the cross-border commodity knowledge graph, combined with the attribute values ​​in the initial standardized commodity information, to automatically generate reliable values ​​for some customs clearance fields through the derivation model and write them into the standard commodity file. At the same time, it generates field tagging information containing recommended values ​​and influencing factors for low-confidence fields. This enables the system to significantly reduce the workload of manual filling of customs clearance fields while ensuring interpretability and traceability, improve the consistency and accuracy of customs clearance field reporting, and provide a higher quality data foundation for subsequent electronic label applications and customs clearance review.

[0076] In some embodiments, the required fields for electronic tag application are extracted based on standard commodity file data to generate electronic tag application data. Electronic tag codes are obtained by interacting with an external electronic tag system through a preset interface, and a correspondence between the electronic tag codes and standard commodity file data is established, including: Based on the interface specifications and compliance rules of the external electronic tag system, select fields related to product identification, packaging specifications, units of measurement, customs codes and customs clearance information from the standard commodity file data. Based on the preset electronic tag field template, map the fields to an electronic tag field set, and attach the file identifier and rule version identifier of the standard commodity file data to the electronic tag field set. Electronic tag application data is constructed based on the electronic tag field set. Field integrity and format consistency checks are performed on the electronic tag application data. A signature digest for message verification is generated based on a preset digest algorithm. The electronic tag application data with the attached signature digest is sent to an external electronic tag system through a secure communication channel. Receive response messages from external electronic tag systems, parse the electronic tag code and its version identifier from the response messages, and establish a mapping relationship between the electronic tag code and its version identifier and the corresponding standard commodity file data's file identifier and rule version identifier.

[0077] Specifically, the electronic tag integration module first selects the set of fields related to the electronic tag application from the standard commodity file data, based on the interface specifications of the external electronic tag system and the compliance rules maintained internally by the system. The interface specifications of the external electronic tag system typically require that the message must include the commodity identification field, packaging specification field, unit of measurement field, customs code field, and a set of fields related to customs clearance information, such as the name of the goods to be cleared, material, chemical composition, density, purpose, and applicable population.

[0078] Based on these requirements, the electronic tag docking module retrieves the corresponding field values ​​from the standard commodity file data and maps "commodity ID", "standardized commodity name", "packaging specifications", "unit of measurement", "standardized customs code", "customs clearance information field" and other fields to the standard field names in the electronic tag application field set according to the preset electronic tag field template.

[0079] For example, for a certain electric kettle product, the field template will map the "product file ID" in the standard product file to the "goodsArchiveId" in the label application, and map "packaging specifications = 1 unit / box", "unit of measurement = unit", and "customs code = 851679××××" to the corresponding application fields. At the same time, the file identifier of the standard product file data and the rule version identifier on which the file was generated will be added to the electronic tag field set to enable accurate mapping between the electronic tag code and the specific file version in the future.

[0080] Furthermore, after completing the construction of the electronic tag field set, the electronic tag integration module assembles the electronic tag field set into electronic tag application data according to the message structure defined in the external interface specification. To ensure that the data complies with regulatory requirements and system verification rules, the module performs field integrity checks and format consistency checks on the electronic tag application data before sending it. Field integrity checks are used to check whether fields marked as mandatory in the interface specification are missing from the application data, such as product identifiers, customs codes, and packaging specifications; format consistency checks are used to check whether the values ​​of each field conform to the expected type and format, such as whether the unit of measurement is within the allowed enumeration range, whether numeric fields are valid values, and whether the customs code is a numeric string of the specified length.

[0081] After successful verification, the electronic tag integration module generates a signature digest for the entire application data based on a preset digest algorithm. For example, it concatenates the message body fields in a specified order, performs digest calculation, obtains a fixed-length verification value, and appends this signature digest to the electronic tag application data. The introduction of the signature digest allows external electronic tag systems to calculate a digest using the same algorithm after receiving a message and compare it with the signature digest carried in the message, thereby determining whether the message has been tampered with during transmission.

[0082] In some examples, electronic tag application data is sent to an external electronic tag system via a secure communication channel. This secure communication channel can employ a network connection with encryption and authentication mechanisms, such as an encrypted connection based on Transport Layer Security (TLS) or a secure interconnection established through a dedicated channel. This ensures that messages containing sensitive information such as product identification, customs codes, and customs clearance information are not intercepted or modified by unauthorized third parties during data transmission. When initiating a request, the electronic tag integration module sets auxiliary fields such as authentication information, timestamps, and request numbers according to interface requirements to enable external systems to perform identity verification and request idempotency control.

[0083] Furthermore, after sending the electronic tag application data, the electronic tag integration module receives a response message from the external electronic tag system. The response message typically includes the application processing status, the electronic tag code, and possibly a code version identifier. The module first verifies the integrity and validity of the response message, such as checking if the returned status is successful and whether the signature or verification fields in the response meet expectations. Then, it parses the electronic tag code and code version identifier from the response message.

[0084] The code version identifier can be used to indicate the coding rule version or allocation strategy version to which the electronic tag code belongs, facilitating the traceability of the specific code's generation basis even after regulatory rule adjustments or coding system upgrades. The electronic tag integration module then establishes a mapping relationship between the electronic tag code and its version identifier and the corresponding standard commodity file data's file identifier and rule version identifier, writing this mapping relationship into the tag mapping storage structure. In this way, when subsequent customs declarations, regulatory traceability, or reconciliation inquiries are required, the system can quickly locate the corresponding electronic tag code and its version information through the standard commodity file identifier.

[0085] Through the above embodiments, the electronic tag docking module introduces technical means such as electronic tag field templates, signature digests, secure communication channels, and encoding version identifiers on the basis of standard commodity file data. It realizes the entire process of automatically extracting electronic tag application fields from standardized commodity data, constructing compliant application messages, and reliably interacting with external electronic tag systems. This establishes a traceable mapping relationship between electronic tag codes and specific standard commodity files and rule versions. In this way, while ensuring the security and integrity of messages, it reduces the workload and error rate of manually compiling electronic tag application data, and improves the automation level of electronic tag application and management for cross-border commodities under the target regulatory environment.

[0086] In some embodiments, the system further includes: The quality assessment module is used to collect information on customs clearance results, electronic tag application results, and merchants' modification records of standard commodity file data, and generate feedback data. Based on the feedback data, the module updates and adjusts the cross-border commodity knowledge graph, the recognition model in the semantic recognition module, and the compliance rules information.

[0087] Specifically, to facilitate subsequent processing, the quality assessment module first standardizes and categorizes the aforementioned raw data, converting results from different sources into a unified feedback data format. During this process, the module can introduce concepts such as "feedback events" and "error type labels." Feedback events describe a single instance of customs clearance, electronic tag application, or file modification for a product at a specific point in time. Error type labels summarize the types of problems exposed in the feedback event, such as "category identification error," "inappropriate customs code selection," "missing customs clearance fields," "field values ​​not conforming to constraints," and "incorrect electronic tag message format."

[0088] For customs clearance results, the module maps the reasons for failure to the corresponding error type tags based on the reasons for order cancellation or supplementary information, and records the standard commodity file identifier, rule version identifier, and used electronic tag code associated with this customs clearance. For electronic tag application results, the module labels the reasons for failure as "missing electronic tag field" or "invalid field format" based on the error codes or error messages returned by the external system. For merchant modification records, the module focuses on identifying the merchant's adjustments to category fields, attribute fields, customs clearance fields, and customs code fields, regarding these adjustments as "correction" signals to the original identification results, and constructs corresponding feedback events accordingly.

[0089] After generating feedback data in a unified format, the quality assessment module aggregates and statistically analyzes the data according to its scope of impact and priority. On one hand, the module can statistically analyze order cancellation rates, electronic tag application failure rates, and merchant manual modification rates by category, customs code, rule version, etc., to identify systematic biases in the identification model or rule configuration under certain categories. On the other hand, the module can also statistically analyze the frequency of errors and associated conditions for specific fields, such as "a certain customs clearance field is frequently missing or rewritten by merchants under a certain category." Based on these statistical results, the quality assessment module generates weighted scores for various error types and distributes the feedback data to different update channels according to categories, including the cross-border commodity knowledge graph update channel, the multimodal semantic recognition model update channel, and the compliance rule information adjustment channel.

[0090] Regarding knowledge graph updates, the quality assessment module can revise entity relationships in the cross-border commodity knowledge graph based on recurring erroneous matching relationships or corrected field values ​​from merchants in the feedback data. For example, if a certain type of commodity has consistently used a particular customs code and cleared customs smoothly in historical customs clearance processes, but the system's original customs code candidates based on the knowledge graph deviate significantly from this, the module will increase the association weight between this category and the actually used customs code, while decreasing the association weight of the original candidate codes or marking them as low-reliability relationships.

[0091] Regarding multimodal semantic recognition model updates, the quality assessment module can record "original model recognition results" and "final merchant confirmation results" in pairs for incremental training of the recognition model. This makes the model more closely resemble the standards actually used by merchants and regulators when recognizing similar products in the future. For compliance rule information, the quality assessment module monitors situations where certain rule description units lead to frequent field omissions or overly strict constraints in actual application. It then prompts rule maintainers to adjust the corresponding mandatory field relationships, conditional trigger relationships, or value constraints to make the rule configuration more closely reflect real regulatory requirements and business operation habits.

[0092] Through the above embodiments, the quality assessment module uniformly converts customs clearance results, electronic tag application results, and merchant modification records into structured feedback data. It also makes targeted updates and adjustments to the cross-border commodity knowledge graph, the recognition model in the semantic recognition module, and compliance rule information. This allows the system to continuously correct commodity recognition results and field constraint configurations based on feedback during actual operation, thereby enhancing the consistency between standardized processing results, regulatory requirements, and actual merchant operations, and improving the accuracy and stability of overall recognition and compliance judgment.

[0093] In some embodiments, the system further includes: The data versioning and evolution management module is used to manage the versions of cross-border commodity knowledge graphs, compliance rule information, recognition model parameters in the semantic recognition module, and standard commodity file data. It assigns version identifiers to the above data objects and records the effective scope of the versions, so as to trace and recalculate the standardization process of different versions when regulatory rules are updated or models are updated. The business interface module is used to generate standardized product profile data based on standard product profile data, electronic tag codes, and version identifiers, and to provide standardized product profile data to merchant systems and downstream business systems through external application interfaces.

[0094] The above embodiments have described in detail the specific modules and functions of the cross-border commodity adaptive compliance data standardization system of this application. The implementation process of the cross-border commodity adaptive compliance data standardization method of this application will be described in detail below with reference to specific embodiments. Figure 2 This is a flowchart illustrating the adaptive compliance data standardization method for cross-border goods provided in this application embodiment, such as... Figure 2 As shown, the method may specifically include the following steps: S201: Obtain raw product data from merchant business systems, partner enterprise resource planning systems, and platform internal systems, and map and convert field names and data formats from different sources to form product input data that conforms to the preset intermediate data structure; S202, based on the text and image information of the goods in the input data, generate a semantic representation of the goods, and combine it with a pre-defined cross-border goods knowledge graph to identify the goods category, brand, general attributes, category-specific attributes and customs code candidates to obtain initial standardized goods information; S203: Obtain regulatory rule texts related to cross-border goods from regulatory rule sources, generate regulatory rule data structures associated with commodity categories, customs clearance fields and customs codes, and transform the regulatory rule data structures into compliance rule information for field item determination and field constraint checks; S204, based on initial standardized commodity information and compliance rule information, determines the target field set of the target commodity under the target regulatory rules, fills in the customs clearance fields that can be deduced from the cross-border commodity knowledge graph and initial standardized commodity information, marks the customs clearance fields that cannot be automatically determined, and generates standard commodity file data containing category information, brand information, attribute information, warehousing and logistics information, adaptation relationship information and customs clearance information. S205: Extract the fields required for electronic tag application based on standard commodity file data, generate electronic tag application data, interact with external electronic tag systems through preset interfaces to obtain electronic tag codes, and establish the correspondence between electronic tag codes and standard commodity file data.

[0095] It should be understood that the sequence number of each step in the above method embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0096] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although the technical solutions of this application have been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A cross-border commodity adaptive compliance data standardization system, characterized in that, include: The data preprocessing module is used to obtain raw product data from merchant business systems, partner enterprise resource planning systems and platform internal systems, and to map and convert field names and data formats from different sources to form product input data that conforms to the preset intermediate data structure. The semantic recognition module is used to generate a semantic representation of the product based on the product text information and image information in the product input data, and to identify the product category, brand, general attributes, category-specific attributes and customs code candidates by combining a predetermined cross-border product knowledge graph, so as to obtain initial standardized product information. The compliance processing module is used to obtain regulatory rule texts related to cross-border commodities from regulatory rule sources, generate regulatory rule data structures associated with commodity categories, customs clearance fields and customs codes, and transform the regulatory rule data structures into compliance rule information for field item determination and field constraint checks. The standard commodity profile generation module is used to determine the target field set of the target commodity under the target regulatory rules based on the initial standardized commodity information and the compliance rule information, fill in the customs clearance fields that can be deduced based on the cross-border commodity knowledge graph and the initial standardized commodity information, mark the customs clearance fields that cannot be automatically determined, and generate standard commodity profile data containing category information, brand information, attribute information, warehousing and logistics information, adaptation relationship information and customs clearance information. The electronic tag integration module is used to extract the fields required for electronic tag application based on the standard commodity file data, generate electronic tag application data, interact with external electronic tag systems through a preset interface to obtain electronic tag codes, and establish a correspondence between the electronic tag codes and the standard commodity file data.

2. The system according to claim 1, characterized in that, The mapping and conversion of field names and data formats from different sources to form product input data conforming to a preset intermediate data structure includes: Based on the preset field semantic model, semantic features are extracted from the field names and field contents of each original commodity data to obtain the semantic representation of fields related to commodity category, brand, attributes, warehousing and logistics information and customs clearance information; The semantic representation of the field is matched with the intermediate field node in the predetermined cross-border commodity knowledge graph used to describe the preset intermediate data structure, and the field mapping relationship between each original field and the target field in the preset intermediate data structure is determined based on the matching result. According to the field mapping relationship, the original product data is renamed at the field level, converted in terms of data type, and normalized in terms of unit of measurement. The processed fields are then restructured according to the preset intermediate data structure to generate the product input data.

3. The system according to claim 1, characterized in that, The system also includes: The cross-border commodity knowledge graph construction module is used to construct a cross-border commodity knowledge graph based on the commodity input data, historical customs clearance data, customs code data, and customs clearance field definitions. This graph includes commodity entities, category entities, brand entities, customs code entities, customs clearance field entities, adaptation relationship entities, and the relationships between them. The module also updates and maintains the cross-border commodity knowledge graph.

4. The system according to claim 1, characterized in that, The process involves combining a pre-defined cross-border commodity knowledge graph to identify commodity categories, brands, general attributes, category-specific attributes, and customs code candidates, thereby obtaining initial standardized commodity information, including: Based on the product text information and image information, a product semantic representation is generated, and the product semantic representation is mapped to the semantic space corresponding to the cross-border product knowledge graph to obtain a product embedding vector compatible with the graph nodes; In the cross-border commodity knowledge graph, the commodity embedding vector is used as the query vector to retrieve a set of candidate nodes from category nodes, brand nodes, attribute nodes and customs code nodes. Based on the similarity relationship between the candidate nodes and the commodity embedding vector and the preset node association path in the graph, the set of candidate nodes is filtered and combined to determine the target category node, target brand node, target attribute node and target customs code candidate node. Based on the node identifier information of the target category node, target brand node, target attribute node, and target customs code candidate node, generate initial standardized commodity information containing category field, brand field, general attribute field, category-specific attribute field, and customs code candidate field.

5. The system according to claim 1, characterized in that, The process involves obtaining regulatory rule texts related to cross-border goods from regulatory rule sources, generating regulatory rule data structures associated with commodity categories, customs clearance fields, and customs codes, and transforming these regulatory rule data structures into compliance rule information for field item determination and field constraint checks, including: Based on a preset rule semantic model, the regulatory rule text is segmented and semantically annotated to extract semantic fragments of field requirements involving commodity categories, customs clearance fields and customs codes, and generate corresponding rule semantic representations. The semantic representation of the rule is associated and matched with the commodity category node, customs clearance field node and customs code node in the cross-border commodity knowledge graph to construct a set of rule triples with commodity category node, customs clearance field node and customs code node as the main body and field mandatory relationship, field combination relationship and value constraint relationship as the object. A regulatory rule data structure containing rule nodes and the association relationship with commodity category, customs clearance field and customs code is generated. According to the preset rule description template, the rule nodes and relationships in the regulatory rule data structure are converted into rule description units containing field item determination logic and field constraint checking logic. Each rule description unit is assigned a rule version identifier and applicable category scope, forming compliance rule information used to drive the standard commodity file generation module to determine the target field set and perform field constraint verification.

6. The system according to claim 1, characterized in that, The step of determining the target field set of the target product under the target regulatory rule based on the initial standardized product information and the compliance rule information includes: Based on the category field and customs code candidate field in the initial standardized commodity information, rule description units that match the target commodity category range and customs code range are selected from the compliance rule information to form a target rule set; Based on the field item determination logic in the target rule set, a field constraint graph structure is constructed with customs clearance field, basic attribute field, and warehousing and logistics field as nodes, and field mandatory relationship, condition trigger relationship, and field combination relationship as edges; Based on the attribute field values ​​identified in the initial standardized product information, the state of the condition triggering relationship associated with the attribute values ​​is determined. Through traversal and dependency reasoning of the field constraint graph structure, the set of required fields, the set of condition fields, and the set of optional fields under the target regulatory rule are determined respectively. The set of required fields and the set of condition fields are then merged to determine the target field set.

7. The system according to claim 1, characterized in that, The process of filling in customs clearance fields that can be derived from the cross-border commodity knowledge graph and the initial standardized commodity information, and marking customs clearance fields that cannot be automatically determined, includes: Based on the category field, attribute field, and customs code candidate field in the initial standardized commodity information, the clearance field nodes that have an associated path with the target commodity category node, target attribute node, and target customs code candidate node are retrieved in the cross-border commodity knowledge graph to construct a clearance field candidate set for the target commodity. For each customs clearance field node in the candidate set of customs clearance fields, the corresponding derivation confidence is calculated using a preset derivation model based on the field value patterns, field dependencies, and attribute values ​​recorded in the cross-border commodity knowledge graph and the initial standardized commodity information. The customs clearance fields with a derivation confidence level higher than the first preset threshold are written into the standard commodity file data, and the customs clearance field filling value is calculated according to the value pattern in the cross-border commodity knowledge graph or the relevant fields in the initial standardized commodity information. At the same time, the field source identifier and rule reference identifier are recorded for each filled customs clearance field. Customs clearance fields with a derivation confidence level lower than the second preset threshold are set to a pending confirmation state, and field tagging information is generated for customs clearance fields in the pending confirmation state. The field tagging information includes recommended values, derivation confidence level, and attribute field identifiers that affect the derivation of the customs clearance field. The first preset threshold is higher than the second preset threshold.

8. The system according to claim 1, characterized in that, The process of extracting the required fields for electronic tag application based on the standard commodity file data, generating electronic tag application data, interacting with an external electronic tag system through a preset interface to obtain the electronic tag code, and establishing the correspondence between the electronic tag code and the standard commodity file data includes: According to the interface specifications of the external electronic tag system and the compliance rule information, select fields related to product identification, packaging specifications, unit of measurement, customs code and customs clearance information from the standard commodity file data. According to the preset electronic tag field template, map the fields to an electronic tag field set, and attach the file identifier and rule version identifier of the standard commodity file data to the electronic tag field set. Electronic tag application data is constructed based on the electronic tag field set. Field integrity and format consistency checks are performed on the electronic tag application data. A signature digest for message verification is generated based on a preset digest algorithm. The electronic tag application data with the attached signature digest is sent to the external electronic tag system through a secure communication channel. The system receives a response message from the external electronic tag system, parses the electronic tag code and its encoding version identifier from the response message, and establishes a mapping relationship between the electronic tag code and the encoding version identifier and the corresponding file identifier and rule version identifier of the standard commodity file data.

9. The system according to claim 1, characterized in that, The system also includes: The quality assessment module is used to collect information on customs clearance results, electronic tag application results, and merchant modifications to the standard commodity file data, and generate feedback data. Based on the feedback data, the module updates and adjusts the cross-border commodity knowledge graph, the recognition model in the semantic recognition module, and the compliance rules information.

10. A method for adaptive compliance data standardization of cross-border commodities based on the system described in any one of claims 1 to 9, characterized in that, include: It acquires raw product data from merchant business systems, partner enterprise resource planning systems, and platform internal systems, and maps and transforms field names and data formats from different sources to form product input data that conforms to the preset intermediate data structure. Based on the product text and image information in the product input data, a semantic representation of the product is generated. Combined with a predetermined cross-border product knowledge graph, the product category, brand, general attributes, category-specific attributes, and customs code candidates are identified to obtain initial standardized product information. Obtain regulatory rule texts related to cross-border commodities from regulatory rule sources, generate regulatory rule data structures associated with commodity categories, customs clearance fields, and customs codes, and transform the regulatory rule data structures into compliance rule information for field item determination and field constraint checks; Based on the initial standardized commodity information and the compliance rule information, the target field set of the target commodity under the target regulatory rule is determined. The customs clearance fields that can be deduced from the cross-border commodity knowledge graph and the initial standardized commodity information are filled in, and the customs clearance fields that cannot be automatically determined are marked. Standard commodity file data containing category information, brand information, attribute information, warehousing and logistics information, adaptation relationship information and customs clearance information are generated. Based on the standard commodity file data, the fields required for electronic tag application are extracted, electronic tag application data is generated, and electronic tag codes are obtained by interacting with external electronic tag systems through a preset interface. The correspondence between the electronic tag codes and the standard commodity file data is then established.

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